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location | https://www.cancerimagingarchive.net/collection/prostate-anatomical-edge-cases/ |
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Image AddedIn this single institution retrospective study, we reviewed 950 consecutive patients with prostate adenocarcinoma receiving definitive radiotherapy between 2011 and 2019, and identified among them 112 patients with anatomic variations (edge cases) seen on simulation CT and/or MRI imaging. These variations included hip arthroplasty, prostate median lobe hypertrophy, so-called “droopy” seminal vesicles, presence of a urinary catheter, and others. Three commercially available solutions for pelvic anatomy autosegmentation were applied to treatment planning CT scans to generate segmented volumes of the prostateA separate cohort of 19 “normal” cases were randomly selected for inclusion. Prostate, rectum, bladder, and bilateral femoral heads . To quantify the accuracy of software-generated contours, Dice similarity coefficients were calculated for each software-generated contour with a comparator clinician-delineated reference contour.Results: On average, deep learning autosegmentation outperformed both atlas-based and model-based were manually segmented on all CT simulation images (where present) and were ultimately used clinically for radiation treatment planning. We leveraged this imaging dataset to assess the comparative performance of deep learning, atlas-based, and model-based autosegmentation methods across both normal and edge case cohorts . However, all methods exhibited lower mean DSC for edge cases compared to the normal cohort, recapitulating certain known algorithm limitations (e.g. in the context of hip prostheses) and identifying novel areas of poor performance.Conclusions: Anatomic variation may present challenges to commercial autosegmentation solutions, and such edge cases should be considered both prior to and throughout any clinical implementation. |
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
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Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
: https://doi.org/10.1016/j.phro.2023.100413. In this paper and in the figure on the right, we show the Cross-sectional CT-based anatomy and autosegmentation performance for representative edge cases. A) Hypertrophic prostate edge case. Each panel depicts a focused excerpt from a single CT scan, centered about two different structures (prostate, bladder) in three different planes (axial, sagittal, coronal). Clinician-delineated “ground truth” contours (MD) for each structure are shown in red, while atlas-based (AB), model-based (MB), and deep-learning based (DL) autosegmented contours are depicted in green, orange, and blue, respectively. Numerical values represent DSC for the corresponding autosegmented volumes compared to MD volumes. B) So-called “droopy” seminal vesicles edge case. Each panel depicts a focused excerpt from a single CT scan, centered about the prostate in two different planes (axial, sagittal). All colors and labeling are as in Panel A) |
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title | Data Access |
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Images and Radiation Therapy Structures (DICOM, XX.X 17 GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/145753229/Prostate-Anatomical-Edge-Cases%20May%202023%20manifest.tcia?api=v2 |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Prostate-Anatomical-Edge-Cases |
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title | Detailed Description |
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| Detailed DescriptionImage Statistics | Radiology Image Statistics |
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Modalities | CT, RTSTRUCT | Number of Patients | 131 | Number of Studies | 131 | Number of Series | 262 | Number of Images | 23,490 | Images Size (GB) | 17 |
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| draft doi: Thompson, R. F., Kanwar, A., Merz, B., Cohen, E., Fisher, H., Rana, S., Claunch, C., & Hung, A. (2023). Stress-Testing Pelvic Autosegmentation Algorithms Using Anatomical Edge Cases (Prostate Anatomical Edge Cases) (Version 1) [Data set]. The Cancer Imaging Archive.https://doi.org/10.7937/FqstfQSTF-st65ST65 |
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| Required acknowledgements only (ex:The CPTAC program requests that publications using data from this program...). If they just want to thank someone, that goes in the Acknowledgement section underneath the Summary.Kanwar, A., Merz, B., Claunch, C., Rana, S., Hung, A., & Thompson, R. F. (2023). Stress-testing pelvic autosegmentation algorithms using anatomical edge cases. In Physics and Imaging in Radiation Oncology (Vol. 25, p. 100413). Elsevier BV. https://doi.org/10.1016/j.phro.2023.100413 |
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| Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC.https://doi.org/10.1007/s10278-013-9622-7 |
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| Version X 1 (Current): Updated yyyy2023/mm05/dd18Data Type | Download all or Query/Filter | License |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Prostate-Anatomical-Edge-Cases |
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